• antibiotic resistance;
  • bacterial evolution;
  • fitness cost;
  • hypermutation;
  • horizontal gene transfer;
  • HGT


  1. Top of page
  2. Abstract
  3. Introduction
  4. Functional role of antibiotics and antibiotic resistance genes in natural environments
  5. Regulation of evolvability and acquired antibiotic resistance
  6. Effect of resistance in bacterial physiology
  7. Global regulatory networks and antibiotic resistance
  8. Metabolic control of antibiotic resistance
  9. Acknowledgements
  10. References

Antibiotic resistance is one of the few examples of evolution that can be addressed experimentally. The present review analyses this resistance, focusing on the networks that regulate its acquisition and its effect on bacterial physiology. It is widely accepted that antibiotics and antibiotic resistance genes play fundamental ecological roles – as weapons and shields, respectively – in shaping the structures of microbial communities. Although this Darwinian view of the role of antibiotics is still valid, recent work indicates that antibiotics and resistance mechanisms may play other ecological roles and strongly influence bacterial physiology. The expression of antibiotic resistance determinants must therefore be tightly regulated and their activity forms part of global metabolic networks. In addition, certain bacterial modes of life can trigger transient phenotypic antibiotic resistance under some circumstances. Understanding resistance thus requires the analysis of the regulatory networks controlling bacterial evolvability, the physiological webs affected and the metabolic rewiring it incurs.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Functional role of antibiotics and antibiotic resistance genes in natural environments
  5. Regulation of evolvability and acquired antibiotic resistance
  6. Effect of resistance in bacterial physiology
  7. Global regulatory networks and antibiotic resistance
  8. Metabolic control of antibiotic resistance
  9. Acknowledgements
  10. References

Bacterial pathogens, once susceptible to antimicrobial agents, are becoming increasingly resistant. In addition, hospitals are now facing problems caused by new opportunistic pathogens (from nonclinical environments) that show little susceptibility to antibiotics (Quinn, 1998; Gaynes & Edwards, 2005). Increasing antibiotic resistance poses important risks to human health (Cohen, 1992; Levy, 1998; Levy & Marshall, 2004); it can affect the course of infectious disease (World Health Organization, 1999) and increase the danger associated with immunosuppression (e.g. in transplantation and anticancer chemotherapy), intubation, catheterization and other common procedures, all of which rely on antibiotics to overcome the infections with which they are commonly associated. As stated by the World Health Organization, the spread of antibiotic-resistant bacteria at hospitals ‘means that commonplace medical procedures once previously taken for granted could be conceivably consigned to medical limbo. The repercussions are almost unimaginable’ (World Health Organization, 2000).

Nonetheless, antibiotic resistance is one of the few examples of evolution that can be studied in real time (Martinez et al., 2007; Courvalin, 2008); this is the focus of the present review [the danger to health and the basic mechanisms of resistance have been reviewed elsewhere (Cohen, 1992; Levy, 1998; Levy & Marshall, 2004)]. This paper does not, therefore, use the clinical definition of resistance based on minimal inhibitory concentration breakpoints (Turnidge & Paterson, 2007), but rather adopts the wider view that it is represented by any mechanism that reduces susceptibility to antibiotics. Antibiotic resistance can come about either as the consequence of genetic change (mutations) (Martinez & Baquero, 2000) or by the acquisition of antibiotic resistance genes (Davies, 1997) through horizontal gene transfer (HGT). Some organisms have a characteristic phenotype of low susceptibility to antibiotics (Bonomo & Szabo, 2006; Fajardo et al., 2008), acquired before the use of antibiotics in medicine.

A global analysis of the phenomenon of antibiotic resistance, understood as an example of bacterial adaptation to stressful conditions (Hamilton-Miller, 2004), requires that the different features of this process be addressed. These include the physiological role of resistance determinants, the regulatory networks that modulate genetic events leading to resistance (namely mutation, recombination and HGT), the overall effect on the bacterial physiology of the acquisition of resistance and the existence of global metabolic networks that mediate bacterial susceptibility to antibiotics. Nonpathogenic bacteria may contain many determinants that can afford resistance, and yet just a few are encountered in pathogen populations (Martinez et al., 2007). Understanding the evolutionary process behind resistance requires a global comprehension of the genetic causes and physiological consequences of its acquisition (Martinez & Baquero, 2002; Martinez et al., 2007).

Functional role of antibiotics and antibiotic resistance genes in natural environments

  1. Top of page
  2. Abstract
  3. Introduction
  4. Functional role of antibiotics and antibiotic resistance genes in natural environments
  5. Regulation of evolvability and acquired antibiotic resistance
  6. Effect of resistance in bacterial physiology
  7. Global regulatory networks and antibiotic resistance
  8. Metabolic control of antibiotic resistance
  9. Acknowledgements
  10. References

Although all environments may be said to be equally natural, this review uses the terms ‘natural environments’ or ‘environmental bacteria’ to describe those with no human-associated (infective or commensal) evolutionary link. Rapid evolution towards resistance to antibiotics (the consequence of their use in the treatment of human infections) is unlikely in environmental bacteria; this contrasts sharply with the situation for human pathogens.

Antibiotics as signals

The search for antibiotics in natural ecosystems was originally based on the idea that the latter contained compounds that inhibited the growth of pathogens; certainly, pathogens are rarely found in the environment (Waksman & Woodruff, 1940). The extraordinary success of this approach, plus the fact that several of the antibiotics used for treating infections are synthesized by soil microorganisms, led to the proposition that, in natural environments, antibiotics have the function of inhibiting the growth of competitors – an example of the Darwinian idea of ‘the struggle for life’. Conversely, antibiotic resistance genes must be elements that evolved to avoid the activity of antibiotics; work on the molecular basis of antibiotic production has clearly shown that antibiotic producers usually possess determinants that afford them resistance to the antimicrobial agents they produce (Benveniste & Davies, 1973). The acquisition by pathogens of resistance genes that originated in antibiotic-producing microorganisms [perhaps even via DNA contamination of antibiotic preparations (Webb & Davies, 1993)] still affords a compelling explanation for the rise of antibiotic resistance.

If the only roles of antibiotics and their resistance elements are to serve as weapons and shields, respectively, the study of antibiotic resistance would not require a global approach, an idea this review reiterates throughout. Most of the several thousand works so far published on antibiotic resistance are based on the implicit belief that resistance is a very specific phenomenon, in which any given mechanism of resistance is simply the defence response to the presence of a given antibiotic. However, recent work indicates that the situation is more complex. Whereas on some occasions the weapon/shield role is still a reasonable explanation for the function of antibiotics and resistance determinants in nature (Pang et al., 1994), alternative functional roles for resistance elements are now being proposed.

Antibiotics have been sought as compounds capable of inhibiting the growth of microbial pathogens (Waksman & Woodruff, 1940), and on some occasions they likely play this role in natural environments (de Lorenzo et al., 1984; Raaijmakers et al., 2002; Haas & Defago, 2005). However, it should be remembered that nearly any compound can be toxic when in a sufficiently high concentration. Because the concentrations of antibiotics used for therapy can be much higher than those encountered in natural environments, it may be that, like other toxic compounds, they have a hormetic effect (Fig. 1), i.e. at low concentrations they induce responses in their target organisms that are actually beneficial, but become toxic at high concentrations.


Figure 1.  Hormesis and the effect of antibiotics on bacterial physiology. Hormesis has been defined as the property of a compound that is beneficial at low concentrations and toxic at high concentrations. At low concentrations, antibiotics can trigger the expression of a specific set of genes that may be beneficial to bacteria. However, at higher concentrations, stress responses are induced and bacterial growth is inhibited. The white box indicates the region of beneficial, adaptive responses and the grey box indicates the region of harmful effects.

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It has been suggested that antibiotics might serve as signalling molecules at the low concentrations in which they are usually found in natural habitats (Davies, 2006; Yim et al., 2006a, b, 2007; Fajardo & Martinez, 2008). This suggestion is based on the fact that low concentrations of antibiotics trigger specific transcriptional changes (Tsui et al., 2004; Yim et al., 2006a, b; Fajardo & Martinez, 2008) that are independent of the microbial networks involved in the general stress response (Goh et al., 2002), that these responses can be beneficial for the microorganisms involved (Linares et al., 2006; Dietrich et al., 2008; Fajardo & Martinez, 2008; Gerber et al., 2008) and that some compounds previously characterized as bona fide signalling molecules also have antimicrobial activity (Ji et al., 1997; Deziel et al., 2004; Kaufmann et al., 2005).

Functional roles of antibiotic resistance genes in their original hosts

If antibiotics have functions other than the killing of competitors, antibiotic resistance genes may have functions other than providing resistance. Even in antibiotic producers, the presence of an antibiotic resistance gene does not necessarily imply that its original role was to help resist the action of the antibiotic produced by the host (Nodwell, 2007). For instance, Streptomyces coelicolor encodes proteins similar in sequence and indeed in mode of action to those involved in resistance to vancomycin in human pathogens (Hong et al., 2002), even though this microorganism does not produce glycopeptide antibiotics.

One example of antibiotic resistance determinants with a functional role other than affording resistance is provided by multidrug resistance (MDR) efflux pumps (Fig. 2). These transporters are present in all organisms (Saier & Paulsen, 2001; Piddock, 2006a, b; Lubelski et al., 2007), and the same MDR pumps are present in all strains of a given species (Alonso et al., 1999b; Alonso & Martinez, 2001; Sanchez et al., 2004). In bacteria, it has been shown that besides offering resistance to antibiotics and other toxic compounds (Silver & Phung, 1996; Hernandez et al., 1998; Ramos et al., 2002; Sanchez et al., 2005), they contribute to virulence (Piddock, 2006a, b), to maintaining homeostasis (Lewinson & Bibi, 2001) and to the detoxification of intracellular metabolites (Aendekerk et al., 2005), among other functions. Mutations leading to the constitutive expression of these transporters afford antibiotic resistance (Alonso & Martinez, 1997; Kohler et al., 1997; Ziha-Zarifi et al., 1999; Sanchez et al., 2002a, b) and produce global changes in bacterial metabolism specific for each MDR pump. It is noteworthy that some of these MDR pumps can efflux signal compounds (Evans et al., 1998; Kohler et al., 2001), indicating that signalling networks may be important in triggering antibiotic resistance.


Figure 2.  Structure of a tripartite efflux pump. The tripartite efflux pumps of Gram-negative bacteria are formed by an integral membrane protein, whose activity is commonly linked to the membrane proton motive force, an outer membrane protein and a periplasmic protein. IM, inner membrane; OM, outer membrane.

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Any enzyme capable of modifying a metabolite with a structure similar to a given antibiotic might modify the antibiotic itself and confer resistance. This is the case of chromosomal 2′-N-acetyltransferase in Providencia stuartii. This enzyme, which modifies bacterial peptidoglycan, can inactivate gentamicin because the latter's structure is similar to that of the enzyme's original substrate (Macinga & Rather, 1999). The fact that elements involved in bacterial metabolism can be important in antibiotic resistance is further shown in that a large number of Pseudomonas aeruginosa genes contribute to its characteristic low-susceptibility phenotype (Fajardo et al., 2008). It has also been suggested that β-lactamases, which have a structure similar to penicillin-binding proteins (PBPs, the targets of β-lactam antibiotics), might have originally been PBPs themselves, involved in the synthesis of peptidoglycan. Their activity against β-lactam antibiotics may have been a side effect of their original function (Massova & Mobashery, 1998; Meroueh et al., 2003).

In summary, the fact that antibiotics trigger specific transcriptional responses, together with the role that determinants involved in signal trafficking or bacterial metabolism might have in resistance, supports the idea that antibiotic resistance should be examined from a more global viewpoint. Only then can the bacterial networks involved in resistance, as well as the changes in bacterial physiology associated with a phenotype, be addressed. These changes might be important in the context of human infections and in the survival and dissemination of resistant bacteria.

Regulation of evolvability and acquired antibiotic resistance

  1. Top of page
  2. Abstract
  3. Introduction
  4. Functional role of antibiotics and antibiotic resistance genes in natural environments
  5. Regulation of evolvability and acquired antibiotic resistance
  6. Effect of resistance in bacterial physiology
  7. Global regulatory networks and antibiotic resistance
  8. Metabolic control of antibiotic resistance
  9. Acknowledgements
  10. References

Resistance to antibiotics can be broadly classified as either intrinsic (Hogan & Kolter, 2002; Fajardo et al., 2008; Tamae et al., 2008) (due to the mechanisms present in all the strains of a given bacterial species) or acquired (Davies, 1994; Alekshun & Levy, 2007) (due to the acquisition of a specific mechanism of resistance, the consequence of mutation, recombination or HGT). Studies of experimental evolution using Metazoans as models are virtually impossible because of the time and population sizes required (Navas et al., 2007), but the acquisition of antibiotic resistance by bacteria provides an example of an evolutionary process that can be readily addressed experimentally. Learning about the mechanisms that lead to resistance has implications for understanding evolution as a whole. Because genome changes are the basis of any evolutionary process, the probability of such events occurring represents the first point of control in the development of resistance. In recent years, it has become evident that the ability to evolve [i.e. evolvability – see Kirschner & Gerhart (1998) for an in-depth review of this concept] is a regulated phenomenon that must be understood from a global viewpoint (Pepper, 2003; Colegrave & Collins, 2008; Isalan et al., 2008; Pigliucci, 2008; Schlichting, 2008). Some aspects of the networks regulating evolvability that can lead to the acquisition of antibiotic resistance are discussed below.

Regulation of mutation rates

Bacterial populations tend to have low mutation rates, which helps preserve their genomes and avoid lethal mutations. DNA error correction mechanisms bring bacterial evolution speeds down by around 1000-fold below their theoretical natural speed limit (Zeldovich et al., 2007). Some authors report, however, that the mutation rate (and thus the speed of evolution) of some bacterial subpopulations is higher than normal (LeClerc et al., 1996; Matic et al., 1997; Oliver et al., 2000; Baquero et al., 2004, 2005), mainly due to defects in the DNA repair systems (Oliver et al., 2002). Bacterial hypermutators are more commonly found in populations under stress, including chronic infections (Oliver et al., 2000), or in the presence of strong selective elements such as antibiotics (Macia et al., 2005) or bacteriophages (Pal et al., 2007). The presence of hypermutators increases the probability of acquiring resistance (Macia et al., 2005; Henrichfreise et al., 2007), and antibiotics select for hypermutators (Mao et al., 1997) in such a way that they enhance the probability of resistance appearing (Blazquez et al., 2002).

The modulation of mutation frequencies may also be important in the development of resistance. Some works report increased numbers of antibiotic-resistant mutants in bacterial populations under stress (Alonso et al., 1999a), and that the number of mutants in colonies of nongrowing bacteria can increase. Two mechanisms have been proposed to explain this (Roth et al., 2006): one based on gene amplification (Andersson et al., 1998; Pettersson et al., 2005) and the other on the regulated increase of mutation rates (Gomez-Gomez et al., 1997; Bjedov et al., 2003; Foster, 2005; Saint-Ruf & Matic, 2006). Recently, the faster growth of resistant mutants has been suggested to be the cause of increased mutation frequencies with respect to rifampicin resistance in ‘ageing’ bacterial colonies (Wrande et al., 2008).

Key components of the mismatch repair system are downregulated under the control of the general-stress sigma factor RpoS (Tsui et al., 1997). In addition, the SOS response to DNA damage (Erill et al., 2007) triggers the transcription of several genes, one of which is dinB, which codes for the error-prone DNA polymerase IV (a Y family DNA polymerase) (Kim et al., 1997; Godoy et al., 2007). This enzyme is able to bypass DNA lesions that block chain elongation by replicative DNA polymerase. In addition, PolIV shows less fidelity than proofreading polymerases when operating on undamaged DNA (Wagner et al., 1999). When PolIV is overproduced, it introduces mutations (which may afford antibiotic resistance) at a high rate (Kim et al., 1997). It has been shown that dinB expression forms part of a large regulatory network that includes the LexA-controlled SOS regulon (Kim et al., 1997; Godoy et al., 2007), RpoS polymerase (Layton & Foster, 2003), the histone-like protein HU (Williams & Foster, 2007) and the polyphosphate kinase (Stumpf & Foster, 2005), among others.

Bacterial mutation rates (and thus the probability of mutation-driven resistance) are therefore subject to complex regulation that we are now just beginning to understand. Because this regulation can be triggered by environmental or metabolic signals (Bjedov et al., 2003), it is important to know which situations might increase the mutation rate and thus the likelihood of acquiring resistance. Quinolones (Phillips et al., 1987) and β-lactams (Miller et al., 2004) induce the SOS response, and could therefore increase the mutation rate. Recent work has shown that PBP3 inhibition by ceftazidime induces transcription of the error-prone polymerase PolIV and induces mutagenesis in P. aeruginosa (Blazquez et al., 2006). Although the SOS system regulates the expression of dinB, other elements are also involved. For instance, studies on Escherichia coli have shown that the induction of dinB by β-lactams occurs via a pathway independent of the SOS response (Perez-Capilla et al., 2005), highlighting that different regulatory networks can modulate bacterial mutability. This view is further supported by recent work showing that subinhibitory concentrations of quinolones and aminoglycosides may result in increased mutability in Streptococcus pneumoniae, independent of DinB activity (Henderson-Begg et al., 2006).

Regulation of recombination

Recombination plays a major role in HGT (Thomas & Nielsen, 2005). Both the acquisition of DNA through transformation and the combination of new elements (e.g. antibiotic resistance genes) into transferable replicons (e.g. plasmids) require recombination among DNA sequences. Studies on E. coli have shown that efficient recombination occurs in plasmids when homologous sequences of 25 bp are present. Increasing the length of homology enhances recombination (Lovett et al., 2002). Recombination is therefore favoured among closely related organisms or elements involved in HGT with similar structural features.

Some particularly important features that favour the spread of antibiotic resistance should be mentioned at this point. Firstly, gene-capture units exist that favour the acquisition of exogenous DNA and its incorporation into a single element. The clearest examples are the integrons (Fig. 3), genetic platforms that contain small repetitive sequences (typically 59 bp) that favour recombination and gene recruitment (Hall & Collis, 1995; Rowe-Magnus & Mazel, 2001; Holmes et al., 2003; Mazel, 2006). These are very important in the dissemination of antibiotic resistance genes (Carattoli, 2001; Rowe-Magnus & Mazel, 2002; Rowe-Magnus et al., 2002). Secondly, the need for homologous sequences to trigger recombination can be relaxed under some circumstances. For instance, some hypermutator strains, particularly those with defects in the methyl-directed DNA-repair genes (such as mutS), recombine at high frequencies with divergent DNA (Matic et al., 2000). Thirdly, antibiotics such as ciprofloxacin may stimulate recombination among different sequences (Lopez et al., 2007). All this indicates that recombination rates, like mutation rates, change as a consequence of the structure of microbial populations (hypermutators) and as a response to external inputs, such as antibiotics (Hastings et al., 2004).


Figure 3.  Structure of an integron. Integrons are formed by a gene coding for a site-specific recombinase known as integrase (intl1), an attachment site (attI) and a strong promoter that drives the expression of the genes located downstream from the attachment site (P). Some conserved genes are present at the 3′-end of integrons of the same type, suggesting a common origin. The figure shows a Type I integron containing qac, which encodes for a protein involved in resistance to quaternary ammonium disinfectants, sul1 that encodes sulphonamide resistance and orf5, which has no known function. Antibiotic resistance genes can be recruited by site-specific recombination (a) driven by the integrase (Intl1). After gene recruitment (b) the recombination sites are reconstructed, allowing the incorporation of new gene cassettes. This structure allows the formation of integrons containing arrangements of antibiotic resistance genes that can be acquired simultaneously by bacterial pathogens.

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Regulation of DNA uptake

Transformation and conjugation usually involve the translocation of single-stranded DNA through the bacterial envelope (Chen & Dubnau, 2004; Chen et al., 2005). After gaining entry, general mechanisms such as DNA modification/restriction (Tock & Dryden, 2005) or plasmid exclusion (Garcillan-Barcia & de la Cruz, 2008) must be bypassed by the exogenous DNA if it is to be maintained. The mechanisms that regulate bacterial DNA uptake and maintenance have been reviewed elsewhere (Chen et al., 2005; Thomas & Nielsen, 2005); only those aspects concerning resistance are discussed here. Natural competence involves the induction of a complex system (the com regulon) usually regulated by quorum sensing (QS) (Pestova et al., 1996; Cheng et al., 1997; Luo et al., 2003). Notably, it has recently been found that the same peptides that induce competence in closely related members of the streptococci family can kill other members of the same genus, making large amounts of DNA available and increasing the efficiency of lateral gene transfer (Johnsborg et al., 2008). This provides an example of the dual antimicrobial and signalling role of some metabolites (discussed above) (Fajardo & Martinez, 2008).

The simultaneous induction of competence in microorganisms and their predation in the same environment is particularly important for S. pneumoniae, which acquires resistance via HGT from DNA released by related commensal species (Sibold et al., 1994; Reichmann et al., 1997; Hakenbeck, 1998). Streptococcus pneumoniae lacks an SOS-like system, but possesses the recA gene, whose induction by DNA-damaging agents is strictly dependent on the ability of these compounds to trigger competence. It has been suggested that the com regulon of S. pneumoniae might have features similar to those of the SOS response regulon in E. coli (Prudhomme et al., 2006). Because the SOS system is induced by some antibiotics, it might be possible for antibiotics to induce competence in S. pneumoniae. Indeed, it has been demonstrated that aminoglycosides and fluoroquinolones induce competence, and that an intact competence regulatory cascade is needed (induction is lost in comA mutants) (Prudhomme et al., 2006).

Antibiotics can thus accelerate DNA acquisition in S. pneumoniae by two different mechanisms: by killing susceptible neighbouring cells and increasing genetic transformability (Prudhomme et al., 2006). If the bacteria that are resistant to a different type of antibiotic are killed, and their DNA is released, this would increase the probability of S. pneumoniae acquiring that resistance.

Building blocks in HGT-acquired antibiotic resistance and the founder effect

The elements of bacterial mobilomes are reviewed in another article of this special issue; this paper only analyses the features concerned with antibiotic resistance. The acquisition and dissemination of resistance by HGT follows a Chinese-box scheme. Inspection of the elements producing resistance at hospitals (Baquero et al., 2002) shows that there are predominant clones that may contain predominant plasmids, which in turn contain predominant elements (transposons, integrons, etc.) in which antibiotic resistance genes are enclosed (Fig. 4). This hierarchical scheme of the elements involved in resistance dissemination has been discussed in detail elsewhere (Baquero, 2004).


Figure 4.  Building blocks in the acquisition of HGT-mediated antibiotic resistance. The elements involved in HGT have common backbones that can incorporate different modules. Antibiotic resistance can be transferred by plasmids that acquire transposons, which in turn can contain integrons that recruit antibiotic resistance genes. The modular structure of the elements involved in HGT allows for rapid evolution when under antibiotic threat. IS, insertion sequences; tra, transference genes; psk, postsegregational killing; mrs, multimer resolution; rep, replication; cop, copy number control; par, partitioning. The figure of the plasmid is modified from Thomas (2000).

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Although the number and variability of the elements present in natural bacterial populations that might contribute to resistance is huge, only a few are present in human pathogens. For instance, E. coli strains isolated before the era of antibiotics contain the same groups of plasmids as those isolated after the introduction of antibiotics into medicine, the only difference between them being the recruitment of resistance genes in the latter (Datta & Hughes, 1983). Similarly, the diversity of transposons and integrons associated with resistance is not as high as might be predicted from the diversity of these elements in natural ecosystems.

Modularity is a common characteristic of biological systems (Kashtan & Alon, 2005; Pereira-Leal et al., 2006). The rules that govern the combination of modules (Stadler et al., 2001) have been analysed in different types of biological networks (Force et al., 2005; Pereira-Leal et al., 2006; Mete et al., 2008) and, more recently, in the structure of bacterial genomes (Dagan et al., 2008). It has been suggested that, like for other modular systems, there are rules (grammar) governing the association of the blocks required for building a transferable resistance element (Baquero, 2004). This grammar has different components: (1) functional elements: each replicon can make use of the transcriptional and metabolic machineries of certain bacterial groups but not of others; they therefore have host specificity; (2) structural features: DNA structures that favour the combination of some elements but not others. On many occasions, homologous recombination is required, so that only those elements with a high degree of sequence identity are able to combine; (3) ecological aspects: first, only those building blocks coexisting in the same environment can combine; second, the most abundant elements may be favoured; and third, in the presence of strong selection, the first element conferring a selective advantage (antibiotic resistance) has the highest probability of spreading and predominating (Fig. 5) in the bacterial population under antibiotic threat.


Figure 5.  The founder effect in the acquisition of antibiotic resistance genes. Natural environments (grey box) contain a large number of antibiotic resistance genes (letters). Some (Greek letters) cannot be transferred to bacterial pathogens (white box) because they do not share the same ecosystem or because they have not been incorporated into gene-transfer elements compatible with human pathogens (a). Although several resistance genes (Roman letters) can be transferred, only a few are currently present in infectious bacteria (darker grey box in b). If the selective pressure does not change, the same antibiotic resistance genes are maintained and spread in the population (c), impeding the entrance of others that could play a similar role (founder effect). If the selective forces change (e.g. the appearance of new antibiotics belonging to the same family, darkest grey box in d), the original resistance gene evolves into more efficient variants (white ‘a’ letters in d) with different specificities.

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The integrons (Stokes & Hall, 1989) are modular structures with a major role in the development of antibiotic resistance. These gene-recruitment determinants (Fig. 3) have a high impact in clustering antibiotic resistance genes, which may later be transferred to pathogens. Although integrase sequences in natural environments show wide variability (Nield et al., 2001; Elsaied et al., 2007), that of the different integrases involved in integrons containing resistance genes in pathogenic bacteria is low. The Type 1 integrons are the most important (Walsh, 2006). If environment-driven selection of integrons occurs, it may be that this type of integrase is the best adapted to infective environments. However, Type 1 integrases are found in a wide range of pathogens from E. coli to P. aeruginosa, which come from different habitats. The founder effect explains the dissemination of the most common types of integrons in human pathogens. For instance, Type 1 integrons contain a conserved gene coding for resistance to sulphonamides (Stokes & Hall, 1989), a family of antimicrobial agents widely used in the first half of the 20th century. It might be suggested that, under strong selective pressure, the first integron containing a sulphonamide resistance gene was selected, and that this became the ancestor of the integrons now found in clinical settings. It is important to note that sulphonamide resistance is still widespread in the United Kingdom despite national prescribing restrictions (Enne et al., 2001), probably because of the success of these integrons in recruiting other antibiotic resistance genes.

The full sequences of different plasmids of the same family can provide new clues regarding the evolution of the elements involved in the HGT of antibiotic resistance genes. The sequences of five IncW plasmids show that two different pathways were involved in the acquisition of resistance genes by the common plasmid backbone. Three plasmids contain a single Integron I platform, in the same position in each plasmid, with different gene cassettes. In the other two plasmids, resistance arose because of the insertion of several transposons containing antibiotic resistance genes (Revilla et al., 2008). The best explanation for this is that, in the former three plasmids, the integron was inserted once into the IncW backbone, followed by diversification of the integron arrangements, while it evolved independently from an IncW ancestor in the other two plasmids (Revilla et al., 2008).

The number of antibiotic resistance genes in nonclinical environments is huge (Alonso et al., 2001; D'Acosta et al., 2006; Wright, 2007; Martinez, 2008; Sanchez et al., 2008), whereas the variability of HGT-acquired resistance determinants in human pathogens is low. A founder effect (Fig. 5) also explains the presence of some antibiotic resistance genes but not of others in bacterial pathogens. Under stable, strong selective pressure, the first determinant conferring resistance to this pressure would predominate and spread in the population. Only after the selective forces change does diversification of the antibiotic resistance determinants usually occur. This is likely the situation with plasmid-encoded β-lactamases. The first β-lactamase described in bacterial pathogens was TEM-1. This enzyme, which inactivates the first class of β-lactams, has been prevalent for several years in resistant enteric Gram-negative bacilli (Roy et al., 1983), whereas another β-lactamase, SHV-1, is produced by the vast majority of resistant Klebsiella pneumoniae strains (Roy et al., 1983; Livermore, 1995). After the introduction of β-lactamase inhibitors and new generations of β-lactams, a burst in TEM-1- and SHV-1-derived enzymes occurred (Paterson & Bonomo, 2005) – an example of accelerated evolution under stress (Medeiros, 1997). In addition, novel families of β-lactamases (Paterson & Bonomo, 2005) began to be acquired by human pathogens, indicating that a change in selective force is required for the diversification of antibiotic resistance determinants (Fig. 5).

Effect of resistance in bacterial physiology

  1. Top of page
  2. Abstract
  3. Introduction
  4. Functional role of antibiotics and antibiotic resistance genes in natural environments
  5. Regulation of evolvability and acquired antibiotic resistance
  6. Effect of resistance in bacterial physiology
  7. Global regulatory networks and antibiotic resistance
  8. Metabolic control of antibiotic resistance
  9. Acknowledgements
  10. References

It is generally accepted that the acquisition of antibiotic resistance produces a global burden on bacterial metabolism (fitness cost), making resistant bacteria less competitive than their susceptible partners (Andersson & Levin, 1999; Andersson, 2006). This idea is based on the fact that mutations leading to resistance occur in genes that are important for bacterial physiology (lethal targets, transporters, etc.). A modification of the target proteins should cause deadaptation from their normal functional role, leading to less efficient bacterial metabolism. Similarly, acquisition of a resistance gene by HGT, for instance due to plasmid transfer, should be associated with a metabolic cost, the consequence of the replication, transcription and translation of the genes present in the new replicon (Bouma & Lenski, 1988). These fitness costs will be reflected in a reduced growth rate. Thus, most studies on fitness costs have involved in vitro analyses using defined growth media.

Nevertheless, some authors indicate that the effect of antibiotic resistance on bacterial physiology is more complex and more specific than previously thought. For instance, it has been shown that the mutations that compensate for fitness defects are different in vivo and in vitro (Bjorkman et al., 2000), and that the same antibiotic resistance mutation can produce opposite effects in two different strains of the same bacterial species (Luo et al., 2005). To understand in depth the effect of resistance on bacterial physiology, the concepts and tools used in the field of system biology must be used. Current models for analysing bacterial infections are based on the use of bacterial strains that are susceptible to antibiotics. With the increase in resistance in bacterial populations, models using resistant strains – if their physiological properties (including virulence) are different – should be used as well.

Alterations in metabolic networks as the consequence of antibiotic resistance mutations

In this section, and for the rest of this review, the term ‘mutation’ is used in its broadest sense, and includes point mutations, deletions, insertions and intrachromosomal recombination. As stated above, mutations that produce resistance to antibiotics occur in genes that usually play important roles in bacterial physiology. The products of those genes form part of complex and highly integrated metabolic and regulatory networks. Alterations in their activities likely alter the cell's entire metabolism. Recent work has shown, however, that the rewiring of E. coli metabolic networks is usually well tolerated (Isalan et al., 2008). Thus, metabolic alterations arising from antibiotic resistance are not necessarily manifested as generally reduced bacterial fitness for all possible environments. On the contrary, a certain degree of specificity and even gain of fitness might be expected. For instance, a Stenotrophomonas maltophilia mutant that overproduces the MDR efflux pump SmeDEF (Alonso & Martinez, 2000) shows impaired virulence in a Dyctiostelium discoideum model, but is more proficient in the utilization of sugars such as gentibiose, dextrin or mannose as well as the C1 carbon source formic acid (Alonso et al., 2004). This indicates that the rewiring of metabolism has occurred rather than the appearance of any general burden (fitness cost). Some recent data on the metabolic profiling of antibiotic-resistant mutants suggest that this may be common. For example, resistance to rifampin is due to mutations in the gene rpoB that encodes the RNA polymerase β subunit (Trinh et al., 2006). Some authors indicate that changes in the rifampin-binding pocket result in alterations in the interactions of the RNA polymerase of Bacillus subtilis with promoters and transcriptional regulators, leading to important changes in phenotypes for sporulation, germination and competence in DNA-mediated transformation (Maughan et al., 2004). In the latter work, some rifampin-resistant mutants, but not others, showed reduced growth rates in a rich medium. However, the mutants were found to be capable of using β-glucosides, nutrients present in soil [produced from decomposing plant matter through the breakdown of celluloses and hemicelluloses (Zhang et al., 2007)], more effectively than their susceptible counterparts (Perkins & Nicholson, 2008). Therefore, the metabolic rewiring produced by rifampin resistance likely led to greater fitness of the resistant mutants (compared with the wild-type strains) for the soil environment.

Enhanced fitness of antibiotic-resistant mutants for a given environment is not as rare as previously thought. Bacteria can acquire compensatory mutations that reduce fitness costs (Bjorkman et al., 2000; Andersson, 2006), but sometimes resistance confers an advantage, even in the absence of selective pressure. For example, high-level quinolone resistance is mainly due to mutations in the topoisomerase genes, which encode the targets of this family of antibiotics (Martinez et al., 1998; Piddock, 1999). Because topoisomerases regulate DNA supercoiling, and DNA supercoiling may regulate gene expression (Galan & Curtiss, 1990; Aleixandre et al., 1991; Dorman, 1991), it is likely that mutations in the topoisomerases lead to changes in the bacterial transcriptome. Some mutations in S. pneumoniae topoisomerases are associated with fitness costs when measured in in vitro tests, whereas other mutations have no associated costs (Johnson et al., 2005; Rozen et al., 2007; Balsalobre & de la Campa, 2008). This is consistent with the fact that different mutations in the topoisomerases alter their activity by different degrees, changing global bacterial metabolism in different ways. It might be predicted that only those mutants with low costs should predominate, which is often the case. However, the situation is generally more complex. Work involving an in vivo model of chicken colonization by Campylobacter jejuni has shown that a single-point quinolone-resistance mutation in the gyrA gene can produce enhanced persistence in the avian host (Luo et al., 2005). Although the basis for this increased fitness has not been studied in depth, it is conceivable that transcriptional changes caused by alterations in the activity of the topoisomerase GyrA affect the expression of the C. jejuni genes involved in chicken colonization. This enhanced fitness is host specific. In fact, the same mutation in two different C. jejuni strains belonging to the same clonal complex had opposite effects: enhanced persistence for one and reduced fitness for the other (Luo et al., 2005). Because the metabolic and regulatory networks of different strains of the same bacterial species can have subtle differences with effects on their interaction with the host (Carilla-Latorre et al., 2008), network rewiring might also have different consequences. This type of strain-specific response to an external input is manifested by the very different transcriptomic changes shown by two strains of P. aeruginosa in response to the same type of oxidative stress (Salunkhe et al., 2005).

Two conclusions can be drawn from the above studies: (1) the effect of high-level quinolone resistance on bacterial physiology is specific for the mutation and strain involved and (2) small genetic changes, such as single-point mutations leading to antibiotic resistance, may produce dramatic, yet specific, changes in bacterial physiology.

The specificity of bacterial responses to mutations leading to resistance is perhaps best explained by studying mutants that overproduce MDR efflux pumps. Pseudomonas aeruginosa has several genes coding for them. The expression of MDR pumps is commonly downregulated (Grkovic et al., 2002), but mutations in their transcriptional regulators may lead to their constitutive expression and hence reduced susceptibility to several antibiotics. Because MDR pumps efflux a wide range of substrates it is conceivable that their constitutive expression might produce a global, nonspecific metabolic burden – the consequence of the noncontrolled release of metabolites from bacterial cytoplasm. Some authors report, however, that the effect of MDR overproduction is specific for each MDR pump. For instance, constitutive overexpression of MexAB-OprM or MexEF-OprN is associated with defects in P. aeruginosa QS because these MDR pumps efflux QS autoinducers (Evans et al., 1998; Kohler et al., 2001). On the other hand, overexpression of either MexEF-OprN or MexCD-OprJ, but not of MexXY or MexAB-OprN, reduces P. aeruginosa Type 3 secretion (T3S) and cytotoxicity independent of the QS response (Linares et al., 2005). Finally, mutants overexpressing either MexAB-OprM or MexCD-OprJ produce more biofilm in in vitro static assays than their parental wild-type strain (Sanchez et al., 2002b).

The above results show that the effect of overproducing one MDR pump or another is rather specific and not the consequence of a general metabolic burden. Work at our laboratory (J.F. Linares et al., unpublished data) indicates that the overexpression of MDR pumps can lead to global changes in the P. aeruginosa transcriptome, and that these changes show a high degree of specificity. An important question to address is the effect of such changes on P. aeruginosa virulence. It was earlier stated that overproduction of MDR pumps reduces this (Cosson et al., 2002; Hirakata et al., 2002), at least in models that resemble acute infection. However, P. aeruginosa is a common cause of chronic infection and the virulence factors required for producing chronic and acute infections differ (Goodman et al., 2004; Ventre et al., 2006). Isolates from acute infections are commonly cytotoxic and produce large amounts of proteases and siderophores but little in the way of biofilms. In contrast, the evolution of P. aeruginosa during chronic infection renders strains with the opposite phenotype (Martinez-Solano et al., in press). It should be noted that a mutant overproducing MexCD-OprJ has low cytotoxicity, produces only small amounts of protease and is impaired in T3S (and thus cytotoxicity) – the phenotypes observed in P. aeruginosa strains obtained from chronic infections. It has been shown that the causes of quinolone resistance in P. aeruginosa involved in acute infections usually involve mutations in the topoisomerase genes, whereas overproduction of MDR pumps is common in isolates from chronic infections (Jalal et al., 2000; Henrichfreise et al., 2007). Further, experimental antibiotic therapy with ciprofloxacin in P. aeruginosa-infected mice selects MexCD-OprJ overproducers (Macia et al., 2006). Although these results are not fully conclusive, they suggest that strains of P. aeruginosa overproducing MexCD-OprJ might be more fit than other antibiotic-resistant mutants with respect to the production of chronic infection.

Because antibiotic resistance may be linked to global bacterial metabolism, genome-scale reconstructions of metabolic networks can provide insights into resistance. This has been addressed in the small-colony-variants (SCVs) of Staphylococcus aureus. SCVs are a type of mutant produced by different bacterial species (Proctor et al., 2006) and are characterized by slow growth, changes in their susceptibility to antibiotics and sometimes changes in their virulence properties. SCVs are frequently found in biofilms (Haussler, 2004; Allegrucci & Sauer, 2007) and in chronic infections (Haussler et al., 1999; von Gotz et al., 2004). The genetic causes of SCVs are variable, but on some occasions, the phenotype is linked to the interruption of the electron transport chain. To analyse the effect of the acquisition of an SCV phenotype, an S. aureus mutant lacking the haeme-requiring cytochrome-b oxidase was constructed and its phenotype was analysed in the context of the reconstructed S. aureus metabolic network (Heinemann et al., 2005). Under anaerobic conditions, wild-type S. aureus produces energy from glucose via fermentation, whereas under aerobiosis it uses glycolysis, the pentose phosphate pathway and the triacarboxylic acid cycle. The predictions of the model, confirmed by experimental data, were that the SCV hemB mutant would have an essentially fermentative metabolism even in the presence of oxygen. Some amino acids such as glutamate, arginine or glutamine enhanced growth by affording additional energy production, while isoleucine, leucine, valine and lysine enhance growth because they can be used directly in protein synthesis. A third group, including glycine and alanine, is not used at all by these bacteria (Heinemann et al., 2005). This evidence supports the idea that resistance in SCV isolates is associated with the rewiring of S. aureus metabolism.

Alterations in metabolic networks as the consequence of acquiring foreign antibiotic resistance genes

Whereas chromosomal mutations leading to resistance likely alter bacterial metabolism, the situation may not be the same with respect to acquired genes. Although on some occasions the expression of HGT-acquired antibiotic resistance genes is regulated (Depardieu et al., 2007), they are sometimes under the control of constitutive promoters (Fig. 3). In other words, these genes do not take part in the same regulatory networks as in their original hosts. Further, the proteins they encode find themselves in a different metabolic context, sometimes without their original biochemical substrates or without the protein partners required for their activity. Under such circumstances, their only function is resistance (if the corresponding antibiotic is present); such situations are said to represent ‘functional shifting’ (Martinez, 2008). Under such circumstances, a global metabolic burden can be expected; in fact, it has been shown that the acquisition of a plasmid produces a metabolic burden that is rapidly compensated by mutations in both the plasmid and the chromosome (Bouma & Lenski, 1988).

However, on some occasions, HGT-acquired antibiotic resistance determinants may encounter a metabolic substrate/protein partner in the new host, so that specific changes in the host's metabolism become possible. For instance, it has been found that the expression of the β-lactamase AmpC by a plasmid leads to a reduction in the growth rate and reduced invasiveness in Salmonella enterica serotype Typhimurium. Changes in colony morphology are also seen (Morosini et al., 2000). Such changes are not observed when the plasmid also contains the ampC repressor AmpR, indicating that they are not the consequence of the metabolic load caused by plasmid replication. Nor are such alterations seen when another β-lactamase is expressed, indicating that the observed effects are not the consequence of metabolic load caused by plasmid translation. It is thus clear that AmpC production reduces the invasiveness of S. enterica in a specific manner. Although not reported, the fact that AmpC may be involved in peptidoglycan synthesis, together with the evidence that protein–peptidoglycan interactions modulate the assembly of the T3S translocon in Salmonella (Pucciarelli & Garcia-del Portillo, 2003), suggests that AmpC overproduction might produce subtle alterations in the bacterial envelope that might alter T3S.

Resistance to glycopeptides in Gram-positive bacteria, mediated by the vanA gene cluster, is a well-studied example of the integration of a resistance determinant into bacterial metabolism (for a review, see Mainardi et al., 2008). This cluster, present in the transposon Tn1546 (Arthur et al., 1993), encodes VanH dehydrogenase, which reduces pyruvate to form d-Lac, and VanA ligase, an enzyme that catalyses the formation of an ester bond between d-Ala and d-Lac. This pathway forms the depsipeptide d-Ala-d-Lac, which is used for the biosynthesis of peptidoglycan with a structure that ends in d-Lac instead of d-Ala (Arthur et al., 1992; Handwerger et al., 1992). The resistance mechanisms are therefore integrated into the physiology of the bacteria. Nevertheless, this integration somehow alters microbial metabolism. The substitution of d-Ala by d-Lac is well tolerated by the enzymes involved in the peptidoglycan assembly, but the modified precursor challenges the in vivo activities of PBPs. For instance, PBP5 is a low-affinity PBP responsible for resistance to ampicillin and third-generation cephalosporins. The induction of VanA-mediated glycopeptide resistance increases susceptibility to β-lactam antibiotics, indicating that the d,d-transpeptidase PBP5 does not function properly with peptidoglycan ending in d-Lac.

Antibiotic-dependent bacterial growth

The existence of bacterial mutants dependent on the presence of an antibiotic for their growth was recognized soon after antibiotics became available in medicine (Gocke & Finland, 1950; Barber, 1953; Worthington et al., 1999). Such dependence does not mean that bacteria use antibiotics as a food resource (Dantas et al., 2008). Because dependence is usually linked to resistance, the underlying reason might be the metabolic rewiring produced by the resistance mechanism now only allowing the growth of the bacterium in the presence of the antibiotic. The causes of antibiotic-dependent growth have been studied in vancomycin-resistant enterococci (Baptista et al., 1997; Sifaoui & Gutmann, 1997; Van Bambeke et al., 1999). As stated above, resistance to glycopeptides involves the formation of a d-Ala-d-Lac depsipeptide. Under strong selective pressure and in the presence of the vancomycin-resistance cluster, the activity of the host d-Ala : d-Ala ligase is not required because the bacteria make use of an alternative ligase involved in resistance (VanA or VanB) that ligates d-Lac to d-Ala. Thus, the ddl gene encoding the host d-Ala : d-Ala ligase accumulates mutations and bacteria can only grow in the presence of the antibiotic because it induces expression of the alternative, antibiotic-resistance-linked ligase (Baptista et al., 1997; Sifaoui & Gutmann, 1997; Van Bambeke et al., 1999).

Remodelling bacterial structure as the consequence of antibiotic resistance

The first careful biochemical studies on the changes in bacterial structure as the consequence of antibiotic resistance examined the effect of penicillin resistance on the structure of pneumococcal cell walls (Garcia-Bustos et al., 1988; Garcia-Bustos & Tomasz, 1990). Penicillin resistance in this bacterial species is due to the molecular remodelling of the cell wall synthesis enzymes (PBPs), and consequently a different biosynthetic activity of the cell wall. It is reported that, while the peptidoglycan of antibiotic-susceptible strains contains monomeric and oligomeric forms of primarily linear stem peptides with the sequence l-Ala-d-iGln-l-Lys-d-Ala (where iGln is isoglutamine), the major peptide species of the cell walls of resistant bacteria are branched-stem peptides carrying Ala–Ser or Ala–Ala dipeptides on the E-amino groups of lysine residues (Garcia-Bustos & Tomasz, 1990). This remodelling of the cell wall structure has been observed for other antibiotics targeting peptidoglycan biosynthesis and has been recently reviewed in Mainardi et al. (2008). Resistance to two classes of antibiotics, namely glycopeptides and β-lactams, strongly modifies the structure of the peptidoglycan in Gram-positive bacteria.

The main conclusion of these studies is that antibiotic resistance, rather than being a general metabolic burden, produces specific changes in the metabolism and in the structure of bacterial pathogens that can only be fully understood from a global perspective.

Global regulatory networks and antibiotic resistance

  1. Top of page
  2. Abstract
  3. Introduction
  4. Functional role of antibiotics and antibiotic resistance genes in natural environments
  5. Regulation of evolvability and acquired antibiotic resistance
  6. Effect of resistance in bacterial physiology
  7. Global regulatory networks and antibiotic resistance
  8. Metabolic control of antibiotic resistance
  9. Acknowledgements
  10. References

Intrinsic antibiotic resistance is a specific feature of any given bacterial species. It is thus conceivable that, like other characteristic phenotypes, it is integrated into global regulatory networks. A good example of the need to study antibiotic resistance from a global perspective is that provided by the protein IgaA. A study of mucoid mutants of S. enterica resistant to mecillinam revealed that mutations in the gene mucM, leading to RcsCDB overexpression, produced mucoidity and antibiotic resistance (Costa & Anton, 2001). Independent work undertaken by another group (Cano et al., 2001) characterized the protein IgaA as an attenuator of intracellular growth required for virulence in S. enterica. It has since been shown (Mariscotti & Garcia-Del Portillo, 2008) that mucM and igaA are the same gene, and that it is simultaneously involved in resistance and virulence in S. enterica. Further work showed that mutations leading to unstable forms of IgaA induce the RcsCDB system, leading to overproduction of colanic acid (Mariscotti & Garcia-Del Portillo, 2008), and that RcsCDB overactivation in IgaA-defective strains attenuates the acute virulence of S. enterica (Tierrez & Garcia-del Portillo, 2004). Later it was shown that RcsCDB provides a phospho-relay system consisting of a hybrid sensor kinase, a phosphotransferase and a response regulator. RcsCDB regulates a large number of bacterial genes (Majdalani & Gottesman, 2007), including those involved in capsule production, which are important in intrinsic resistance to β-lactams such as mecillinam (Laubacher & Ades, 2008). This example shows how two independent but convergent studies, one focusing on virulence and the other on resistance, concluded the involvement of the same elements, illustrating the need to make studies on these topics more integrated. Below, two examples of networks that simultaneously modulate several different bacterial phenotypes, including resistance, are discussed.

The mar regulon

The mar (from multiple antibiotic resistance) regulon (George & Levy, 1983; Cohen et al., 1989) is one of the best-studied bacterial networks involved in antibiotic resistance. Since its first description, the mar system has been identified in several bacterial genera, mainly belonging to Enterobacteriaceae (Cohen et al., 1993a, b; Sulavik et al., 1997; Kunonga et al., 2000; Udani & Levy, 2006). The operon marRAB encodes MarR, a repressor that downregulates marRAB expression, MarA, a global activator that regulates the expression of a large number of genes including marA, and marB, which encodes MarB, a small protein for which an independent effect has not been demonstrated but that increases antibiotic resistance when present alongside MarA (White et al., 1997). MarA regulates, directly or indirectly, the expression of several genes (Barbosa & Levy, 2000) that encode proteins with different functions in general metabolism, DNA repair and translation, pH and superoxide stress responses, and low-level resistance to antibiotics, disinfectants and organic solvents (Cohen et al., 1993a, b; Martin et al., 1996; Alekshun & Levy, 1997, 1999; Pomposiello et al., 2001). Two other regulatory proteins homologous to MarA, SoxS and Rob are able to activate the Mar regulon directly or indirectly.

The use of bioinformatics, genomics and molecular genetics in the analysis of transcriptomic data led to the conclusion that these three transcriptional activators directly activate a common set of 40 promoters in E. coli (Martin & Rosner, 2002). Each activator is regulated by different signals: aromatic weak acids such as salicylate increase marRAB transcription (Cohen et al., 1993a, b), superoxides trigger soxS transcription (Amabile-Cuevas & Demple, 1991; Wu & Weiss, 1991) and bile salts, decanoate and bipyridyl activate Rob (Rosenberg et al., 2003).

The marA/soxS/rob regulon therefore encompasses a complex network that produces a global bacterial response to external signals. The upregulation of the MarA, SoxS and Rob activators increases antibiotic efflux (acrAB-tolC), DNA repair (nfo) and superoxide resistance (zwf, fpr and sodA), and reduces outer membrane permeability (ompF via micF). It is important to note here that the main resistance system regulated by MarA is the efflux pump AcrAB-TolC. This MDR pump can efflux antibiotics and bile salts (Ma et al., 1995; Thanassi et al., 1997; Prouty et al., 2004). Because bile salts are common compounds in the natural habitat of Enterobacteriaceae it is likely that the original function of AcrAB-TolC was the resistance of these detergents. It has now been shown that AcrAB-TolC is required for chicken colonization and virulence by S. enterica (Buckley et al., 2006), highlighting that this MDR determinant has other functions besides resistance.

The rules that govern the differential expression of the members of this network are beginning to be understood. It has been shown recently that both the abundance and the type of activator trigger different responses in the network (Martin et al., 2008). For instance, both MarA and SoxS can saturate in vivo the marRAB and sodA promoters. However, saturation with MarA leads to more marRAB and less sodA expression than does SoxS saturation. The extent to which genes of this operon are activated is a function of the MarA concentration, a phenomenon termed ‘commensurable regulon activation,’ because it allows E. coli to elicit a proportional response to stress signals (the activation of the minimum number of genes required to evade this signal) (Martin et al., 2008). It was further suggested that this type of activation allows bacteria to mount different defensive responses depending on the type of signal received, the amplitude of the signal and its duration. This regulon thus provides a flexible response to different kinds and level of threat, including the presence of antibiotics.

The mgrA regulon

MgrA was simultaneously described by different groups as a regulator of the expression of the MDR pump NorA of S. aureus (Truong-Bolduc et al., 2003), of the autolysis process in this bacterial species (Ingavale et al., 2003) and as a general regulator affecting the transcription of S. aureusα-toxin, nuclease and protein A, among others (Luong et al., 2003). Since then, it has been shown that around 350 genes are regulated by MgrA (Luong et al., 2006). These genes include several MDR pumps (Truong-Bolduc et al., 2003, 2005) and virulence factors (Ingavale et al., 2005). Notably, the expression of several metabolic genes is regulated by MgrA, highlighting the crossconnection between resistance, virulence and the global metabolism of bacterial pathogens.

Although it has been shown that MgrA binds some promoter regions (Truong-Bolduc et al., 2003), several of the observed effects are likely indirect. Moreover, the fact that some genes are upregulated by MgrA in one growth phase while they are downregulated in another indicates that there must be different pathways of regulation of the mgrA regulon (Luong et al., 2006). This indirect effect can be mediated by the activity of MgrA in the regulation of other regulators, as seen in S. aureus autolysis.

Murein hydrolases in staphylococci have important roles in cell division, including daughter cell separation and peptidoglycan recycling. Their expression is tightly controlled because, if they are expressed at too high a level, they destroy the cell wall and cell lysis ensues. It has been shown that MgrA regulates expression of the autolytic regulators lysSR, lgrAB and arlRS (Ingavale et al., 2005). Mutations in mgrA result in enhanced autolysis of S. aureus and increased susceptibility to detergents and penicillin.

As mentioned above, MgrA regulates the expression of several MDR pumps in S. aureus. However, its effect on susceptibility to penicillin is also linked to its role in regulating penicillin-induced autolysis, indicating that multiple layers exist in the regulation of resistance by MgrA. Altogether, this indicates that the antibiotic resistance of S. aureus is under the control of global regulation networks that involve virulence determinants as well as basal metabolic or structural genes.

Metabolic control of antibiotic resistance

  1. Top of page
  2. Abstract
  3. Introduction
  4. Functional role of antibiotics and antibiotic resistance genes in natural environments
  5. Regulation of evolvability and acquired antibiotic resistance
  6. Effect of resistance in bacterial physiology
  7. Global regulatory networks and antibiotic resistance
  8. Metabolic control of antibiotic resistance
  9. Acknowledgements
  10. References

It has already been shown that antibiotic resistance can produce specific changes in bacterial metabolism and that resistance is integrated into global regulatory networks. It is thus conceivable that resistance might be controlled by the metabolic condition of bacteria. The clearest example of this noninherited phenotypic resistance (Martinez et al., 1994; Levin & Rozen, 2006) is the lack of effect of several antibiotics against cells that are not actively dividing (dormant cells). Known as ‘antibiotic indifference,’ this phenomenon was recognized soon after the first use of antibiotics (Lee et al., 1944; McDermott, 1958). It is of no importance in infections produced by actively growing bacteria, but reservoirs of nongrowing bacteria can be important in the persistence and reappearance of some infections. The existence of dormant cells helps to explain the presence of persistent subpopulations in antibiotic-susceptible bacterial populations, and in the phenotypic resistance shown by bacterial biofilms. The global regulation shown by bacteria in response to particular modes of life is also important in noninherited resistance (Levin & Rozen, 2006). This is discussed below.

Swarming motility and global regulation

Swarming is a specific type of motility across a semisolid surface commonly demonstrated by flagellated bacteria (Fraser & Hughes, 1999; Jarrell & McBride, 2008). It is a multicellular phenomenon dependent on bacterial cell density, nutrients conditions and surface moistness.

Swarmer cells show structural changes, including an increase in their number of flagellae or in cell length. They also show substantial alterations in their metabolism, indicating that swarming is a global lifestyle adaptation in response to specific conditions rather than just a form of locomotion. In Salmonella, swarming is associated with phenotypic antibiotic resistance (Kim & Surette, 2003). Recent work has shown that swarming in P. aeruginosa is influenced by a large number of cooperating genes (Overhage et al., 2007), and that swarmer P. aeruginosa cells show altered levels of expression for 407 genes. Several virulence determinants and one MDR efflux pump are also upregulated in swarmer cells, indicating that this phenomenon is a global response that might be useful in host colonization in the presence of several antibiotics. One characteristic feature of swarmer cells is their enhanced resistance to polymyxin B, gentamicin and ciprofloxacin; it has been suggested that this is due to metabolic differentiation during swarming and due to changes in the cell envelope (Overhage et al., 2008).


Persistence is a phenomenon by which bacterial subpopulations enter a refractory state in the presence of different antibiotics (Balaban et al., 2004; Levin, 2004), and has been described in several species. Around one in every million of cells in the exponential growth phase, and around one in every hundred in the stationary growth phase possess a persistence phenotype. Persistent cells can grow when the antibiotic disappears, but, like the susceptible wild-type population, they are killed if the antibiotic is added again (Fig. 6). This indicates that persistence is not the consequence of mutations. It has been suggested that persistence provides a means of survival in changing environments (Kussell et al., 2005).


Figure 6.  Bacterial persistence to antibiotics. Persistence in the presence of an antibiotic is characterized by the presence of a small bacterial subpopulation refractory to the antimicrobial agent. If the antibiotic is removed (arrow), this population resumes growth. If it is added again, growing bacteria are killed, indicating that persistence is a noninherited resistance phenotype.

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It was first suggested that, in asynchronous cultures, persistence was the consequence of the presence of a bacterial subpopulation still in the lag period. These cells are in a dormant state and thus refractory to antibiotics. Single-cell analysis using microfluidic devices allowed two types of persisters to be defined. Type I is represented by the preexisting population of growing cells generated during the stationary phase that did not resume growth. Type II persisters are the consequence of a switch between normal and persister cells that occurred during growth. This switch can be mathematically described in the framework of a simple two-state model (Balaban et al., 2004). Recent work indicates that the dormancy shown by Type I persistence is not completely attributable to a stationary-phase state. Single-cell analysis has shown that protein production takes place over a small time window (around 1.5 h for E. coli) in Type I persisters following their exit from the stationary phase (Gefen et al., 2008). This suggests that the dormancy that protects persisters from antibiotics does not occur during the stationary phase but rather develops after this phase is over. Notably, during this time window, persistent cells are as susceptible as normal cells to antibiotics, indicating that, even for Type I persisters, this phenotype implies a metabolic switch in a subset of the bacterial population (Gefen et al., 2008).

Compelling evidence supports the notion that persistence is highly regulated. Persistence might well be explained by the activity of toxin/antitoxin systems (Keren et al., 2004; Lewis, 2008), whose actions maintain a fraction of the population in a nongrowing state. Recent work confirms this, and indicates that persistence is due to several mechanisms involving the participation of a number of global regulators and metabolic enzymes (Hansen et al., 2008).


Most studies in microbiology are performed with bacteria growing planktonically, but bacterial populations frequently grow attached to surfaces where they form biofilms (Costerton et al., 1999; Hall-Stoodley et al., 2004; Kolter & Greenberg, 2006; Hansen et al., 2007). There is still some debate as to whether a specific program exists for triggering biofilm formation (O'Toole et al., 2000; Stoodley et al., 2002) or whether growth in biofilms is simply due to the physicochemical characteristics of the cell envelopes and substrate, and due to the fluid dynamics (including bacterial motility) of the system (van Loosdrecht et al., 2002). However, it is clear that bacteria growing in biofilms experience changes in microbial metabolism (Whiteley et al., 2001; Sauer et al., 2002; Waite et al., 2005, 2006), including the development of greater resistance to antibiotics than planktonically growing cells (Drenkard & Ausubel, 2002; Davies, 2003). Several hypotheses have been proposed to explain this more resilient phenotype (Stewart, 2002), some of them involving the structure of the biofilm and others involving the metabolic state of biofilm-growing bacteria. For instance, the structure of the biofilm itself might preclude the entrance of antibiotics through the matrices (e.g. oligosaccharides or DNA) that form part of biofilms (Suci et al., 1994). Because biofilms are structured environments (Lawrence et al., 1991) containing regions with different amounts of oxygen and nutrients, the metabolic activity in different parts of the biofilm is likely different (Huang et al., 1995; Sternberg et al., 1999); thus, biofilms are home to bacterial populations with different metabolic statuses. Owing to the scarcity of nutrients, it has been suggested that subpopulations of bacteria forming biofilms enter a dormant state, similar to that seen in antibiotic persisters.

The study of the response of mature, preformed biofilms to antibiotics may provide more insight into noninherited resistance. Resistance in biofilms is dependent on the structure of the biofilm and on the type of antibiotic used (Folkesson et al., 2008). Escherichia coli in mature biofilms are resistant to the antibiotic peptide colistin but not to the fluoroquinolone ciprofloxacin. Increased survival in the presence of polymyxin is linked to modifications in bacterial lipopolysaccharide that favour sequestration of the antibiotic. These modifications are the consequence of upregulation of the yfbE operon mediated by the two-component system basR/basS. This specific response is produced before polymyxin is added to the culture. Thus, it is not a classically induced resistance mechanism but the consequence of the metabolic changes experienced by a bacterial subpopulation in response to as yet ignored signals in given locations of the biofilm structure (Folkesson et al., 2008). It is important to note that the Salmonella typhimurium operon pmrH, which is homologous to yjbE, is induced during swarming (Kim & Surette, 2003), a phenomenon during which reduced susceptibility to antibiotics is also shown.

Recent studies have shown that MDR efflux pumps can specifically contribute to phenotypic resistance in biofilms (Zhang & Mah, 2008). The metabolically heterogeneous nature of biofilms is highlighted by a study showing that P. aeruginosa growing in biofilms is tolerant to ciprofloxacin or tetracycline in the deeper areas, whereas tolerance to colistin is restricted to the upper regions (Pamp et al., 2008). Cells in the deepest regions of biofilms show low metabolic activity, consistent with their refractory state. However, the upper layer of a biofilm contains metabolically active bacteria; thus, resistance to colistin cannot be due to dormancy. It is reported that tolerance to colistin in P. aeruginosa biofilms is dependent on the pmr operon, which mediates lipopolysaccharide modification, and on the MDR pump MexAB-OprM (Pamp et al., 2008). Notably, this efflux pump does not contribute to intrinsic resistance to colistin in planktonic P. aeruginosa cells, although it seems to be important to subpopulations surviving the presence of colistin within P. aeruginosa biofilms (Pamp et al., 2008). All this indicates that the tolerance of biofilms to antibiotics is mediated by several different elements in bacterial subpopulations with different metabolic signatures.

It is thus clear that different bacterial ways of life can trigger the expression of specific sets of genes that reduce susceptibility to antibiotics. Understanding this type of process will help in the fight against noninherited antibiotic resistance.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Functional role of antibiotics and antibiotic resistance genes in natural environments
  5. Regulation of evolvability and acquired antibiotic resistance
  6. Effect of resistance in bacterial physiology
  7. Global regulatory networks and antibiotic resistance
  8. Metabolic control of antibiotic resistance
  9. Acknowledgements
  10. References

Work in our laboratory is supported by grants BIO2005-04278 and BIO2008-00090 from the Spanish Ministerio de Educacion y Ciencia, and LSHM-CT-2005-518152 and LSHM-CT-2005-018705 from the European Union. A.F. is the recipient of a fellowship from the Spanish Ministerio de Educacion y Ciencia. We thank Adrian Burton for help with the English manuscript.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Functional role of antibiotics and antibiotic resistance genes in natural environments
  5. Regulation of evolvability and acquired antibiotic resistance
  6. Effect of resistance in bacterial physiology
  7. Global regulatory networks and antibiotic resistance
  8. Metabolic control of antibiotic resistance
  9. Acknowledgements
  10. References
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